Abstract
Aim: Previous studies have shown that higher educational levels are associated with slower progression of arterial stiffness; however, evidence from Asian countries is lacking. We aimed to examine the association between educational level and arterial stiffness measured using the cardio-ankle vascular index (CAVI) over time in a sample of Japanese men and women.
Methods: A total of 1381 participants (453 men and 928 women) were included in the present study. Arterial stiffness was measured using the CAVI at baseline (2009–2012) and 5 years later (2014–2018). The educational level was divided into two groups (junior or senior high school vs. junior college, professional school, college, or higher) based on a self-administered questionnaire. A mixed-effects model was used to analyze the association between education and the CAVI at baseline and its change over 5 years. The participants were stratified by sex and age (＜65 vs. ≥ 65 years).
Results: The CAVI at baseline did not differ significantly according to education in any of the four subgroups accorded to age and sex. However, among women of ≥ 65 years of age, the change in the CAVI over 5 years was significantly smaller in the higher education group (p=0.04). No such association was found in women of ＜65 years of age or men.
Conclusions: Education is a factor that affects arterial stiffness in women of ≥ 65 years of age. These results suggest that educational level affects arterial stiffness, depending on sex and age.
Introduction
Ischemic heart disease (IHD), stroke, and chronic obstructive pulmonary disease were the top three leading causes of death worldwide in 2019 1). In both men and women in Japan, heart disease and cerebrovascular disease were the second and fourth leading causes of death in 2021, respectively, after malignant neoplasms2). Previous epidemiological studies have confirmed that these life-threatening illnesses are associated with arterial stiffness and atherosclerosis3). Arterial stiffness has been shown to predict cardiovascular disease (CVD) beyond traditional risk factors4) and is likely to predate hypertension and target organ damage, which has important implications for the early identification of individuals at high risk of CVD5).
Previous studies have shown that different socioeconomic contexts and inequality contribute to mortality, morbidity, and biological and behavioral risk factors in Japan6). Education has often been used as an index of socioeconomic status. Lower educational levels have been associated with the incidence of coronary heart disease (CHD) and stroke among women in Europe and the United States7, 8). A Japanese study showed that men and women with a low level of education have an increased risk of cerebrovascular disease9). The study also reported that the risk of circulatory system diseases in women was greater in the low-education group than in the high-education group. Notably, the association between higher educational attainment and either mortality or morbidity is not as strongly expressed among the Japanese as it is in Western countries6).
The association between socioeconomic status and health is considered to be the result of a combination of biological, lifestyle behavioral, environmental, and social factors10, 11). An inverse association between educational attainment and several inflammatory markers (CRP, IL-6, ICAM-1, TNFR2, P-selectin, MCP-1, and fibrinogen) has been reported as a biological mechanism12). Socioeconomic position may be related to health behaviors (e.g., smoking)13) and CHD risk markers, such as hypertension14), and—in the case of women—diabetes15) and obesity16), which can serve as intermediate mechanisms contributing to the emergence of altered concentrations of inflammatory markers12). Psychosocial factors, such as the presence of a social network, social support, and stress at home or in the workplace, may also contribute to the association between educational level and health9). Another possible biological connection is that stress related to lower socioeconomic status elicits changes in the function of the hypothalamic-pituitary-adrenal (HPA) axis17). There is a consistent relationship between chronic emotional or psychosocial stress and coronary atherosclerosis and its risk factors, which persists after adjusting for lifestyle variables18).
Aim
Longitudinal changes in arterial stiffness in relation to education have been reported in a multiethnic cohort in the United States19). This study found that the level of education affected arterial stiffness differently in men and women. It was also reported that a higher educational level was associated with an increased carotid artery distensibility coefficient and decreased Young’s elastic modulus, indicating a protective effect against arterial stiffening over time. However, to our knowledge, no longitudinal study has reported such an association in Asian countries. Therefore, we aimed to examine the association between educational level and arterial stiffness, and any longitudinal changes among community-dwelling people in Japan.
Methods
Study Population
We used data from the Toon Health Study (THS) initiated in 2009. THS is a prospective community-based cohort study conducted in Toon City, Ehime Prefecture, Japan. Toon City is a suburban area located in southwestern Japan and had a population of 34800 in 2009 20). Study participants of 30–79 years of age were recruited from among residents between 2009 and 2012. Participants underwent a physical examination and blood test and completed a questionnaire survey.
Of the 2032 participants enrolled in the baseline study between 2009 and 2012, 1461 participated in a 5-year follow-up study conducted between 2014 and 2018.
Of the 1461 participants who participated in both baseline and follow-up studies, we excluded those who selected “other” for the question regarding educational level (n=10), those who had cardio-ankle vascular index (CAVI) data missing at the baseline (n=1), and those who had been diagnosed with myocardial infarction or stroke (n=69). Ultimately, 1381 men and women were included in the present study (Fig.1). Written informed consent was obtained from all the participants. Ethical approval was obtained from the ethics committee of Juntendo University and Institutional Review Board of Ehime University Hospital.
Measurements
Measurement of the CAVI
The CAVI reflects the degree of arterial stiffness from the heart to the ankles. As atherosclerosis progresses, the CAVI increases. The CAVI was measured according to a standardized method using VaSeraVS 1500E (Fukuda Denshi Co., Ltd., Tokyo, Japan). All CAVI measurements were obtained during the morning hours using cuffs applied to the bilateral upper arms and ankles, with the participant lying in the supine position and their head held in the midline position. The examinations were performed after the participants had rested for 5 min. The CAVI was calculated based on the stiffness parameter β, which represents the natural vascular stiffness independent of blood pressure, as measured using carotid echography.
The pulse wave velocity (PWV) between the heart and ankle was obtained according to the L/T ratio, where L is the distance from the aortic valve to the ankle and T is the time during which the PWV propagates from the aortic valve to the ankle. The scale conversion from PWV to CAVI was performed using the following formula:
CAVI=a {(2ρ/DP) × ln (Ps/Pd) PWV2}＋ b
Where Ps and Pd are the systolic blood pressure and diastolic blood pressure values, respectively; PWV is the pulse wave velocity between the heart and ankle; a and b are constants; DP is Ps minus Pd; and ρ is blood density. This equation was derived from the Bramwell-Hill equation21) and stiffness parameter β22). The scale conversion constants were determined to match the CAVI with the PWV, according to Hasegawa’s method23). After automatically obtaining the measurements, the right and left CAVI values were calculated and analyzed using the VSS-10 software program (Fukuda Denshi Co., Ltd., Tokyo, Japan). The higher value from either the right or left CAVI measurement was logn-transformed for use in the subsequent analysis to improve the normality of the data distribution.
The reproducibility of the CAVI measurements was demonstrated by a 3.8% coefficient of variation (CV); this value is within a satisfactory range, as a CV of 5% is generally accepted to be within the limit for clinical laboratory testing24). Further theoretical details of the CAVI method have been described elsewhere24).
Educational Level
Prior to the 5-year follow-up examination, participants were asked to complete the questionnaire at home, which included a question about their educational level. Each submitted questionnaire was reviewed by trained dietitians to confirm that it was completed satisfactorily. Participants reported their level of school education completed by selecting one of the following options: (1) junior high school, (2) senior high school, (3) junior college graduate, professional school, or 4-year college dropout, (4) college or higher, and (5) other. The last group was excluded from the analysis, and the rest of the participants were categorized into two groups: junior or senior high school as the lower education group, and junior college, professional school, college or higher as the higher education group.
Covariates
Baseline characteristics, including age, body mass index (BMI) calculated as weight divided by height squared, and menopausal status, were recorded in the baseline study. Participants were also asked to complete a questionnaire on demographic factors and lifestyle variables (family structure, smoking, and alcohol consumption) at home prior to the baseline examination, and trained dietitians at the examination site reviewed each of the submitted questionnaires to confirm that they were satisfactorily completed. A medical history of hypertension was defined from the baseline records as systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg and/or treatment for hypertension. Diabetes mellitus was defined as fasting serum glucose ≥ 7.0 mmol/L (126 mg/dL) and/or ≥ 11.1 mmol/L (200 mg/dL) at 2-hour postprandial and/or treatment for diabetes mellitus. Dyslipidemia was defined as fasting serum triglyceride ≥ 1.69 mmol/L (150 mg/dL), LDL-cholesterol ≥ 3.62 mmol/L (140 mg/dL), HDL-cholesterol ≤ 1.03 mmol/L (40 mg/dL), and/or current treatment with hypolipidemic agents. Physical activity levels were assessed at baseline using a validated questionnaire consisting of 14 questions on occupation, locomotion, housework, sleep time, and leisure-time physical activity25). Responses for each physical activity category were converted to metabolic equivalents (METs) according to the compendium developed by Ainsworth et al.26) and expressed as METs·h/day.
Statistical Analyses
We performed an analysis of variance to compare mean values and a chi-squared test to compare proportions in the descriptive analysis of baseline characteristics.
Mixed-effect models were used to examine the effect of lower educational attainment on baseline CAVI and longitudinal changes in CAVI over 5 years in comparison to higher educational attainment. All models included a term for time (individual follow-up in years divided by 5 to yield effects on changes in CAVI over 5 years). The main effect estimates the effect of education on CAVI at baseline, whereas the education×time interaction term estimates the mean difference in the 5-year change in CAVI.
All multivariable analyses were adjusted for baseline variables including age (years), BMI (kg/m2), history of hypertension (yes/no), history of diabetes mellitus (yes/no), history of dyslipidemia (yes/no), family structure (living alone; couple; couple with child(ren); couple with child(ren) and grandchild(ren); or other), current smoker (yes/no), current alcohol intake (yes/no), physical activity level (METs·h/day), menopausal status (only for analyses in women; yes/no), and the interaction term for age at baseline (years) and time (individual follow-up in years divided by 5).
All analyses were stratified by sex and conducted using the SAS software program (version 9.4; SAS Institute Inc., Cary, NC, USA). Further stratification was performed according to age at baseline (＜65 vs. ≥ 65 years). P values of ＜0.05 were considered to indicate statistical significance.
Results
The baseline characteristics, follow-up CAVI, its difference from the baseline of the study sample, and the mean differences in each variable in relation to sex and educational level are presented in Table 1. Among men, the higher education group was younger, less likely to have a history of hypertension, more likely to live with a spouse and child(ren), more likely to drink alcohol, and had lower CAVI on average at baseline, relative to the lower education group. Similarly, women in the higher education group were younger, less likely to have a history of hypertension, more likely to live with their spouse and child(ren), more likely to drink alcohol, and had a lower CAVI at baseline. Women with a high educational level also showed lower BMI and lower physical activity level, were less likely to have a history of dyslipidemia, and a smaller proportion had commenced menopause relative to the lower education group.
Table 1.Basic characteristics according to sex and educational level
|
Men |
Women |
p for difference
|
| Total |
Junior or senior high school
(lower education group)
|
Junior college,
professional school,
college or higher
(higher education group)
|
Total |
Junior or senior high school
(lower education group)
|
Junior college,
professional school,
college or higher
(higher education group)
|
| (n=453)
|
(n=217)
|
(n=236)
|
(n=928)
|
(n=499)
|
(n=429)
|
| Age, years |
59.6 (11.8) |
62.5 (10.5) |
56.9 (12.2) |
57.0 (11.7) |
60.8 (10.4) |
52.6 (11.5) |
＜.001 |
| Body-mass index, kg/m2
|
23.8 (2.7) |
23.6 (2.7) |
24.0 (2.7) |
22.7 (3.3) |
23.0 (3.3) |
22.2 (3.2) |
＜.001 |
| Body-mass index ≥ 27, n (%)
|
55 (12.1) |
26 (12.0) |
29 (12.3) |
93 (10.0) |
56 (11.2) |
37 (8.6) |
0.38 |
| Hypertension, n (%) |
207 (45.7) |
112 (51.6) |
95 (40.3) |
295 (31.8) |
188 (37.7) |
107 (24.9) |
＜.001 |
| Diabetes mellitus, n (%)
|
56 (12.4) |
32 (14.8) |
24 (10.2) |
72 (7.8) |
44 (8.8) |
28 (6.5) |
0.01 |
| Dyslipidemia, n (%) |
220 (48.6) |
104 (47.9) |
116 (49.2) |
416 (44.8) |
253 (50.7) |
163 (38.0) |
＜.001 |
| Family structure, n (%)
|
|
|
|
|
|
|
＜.001 |
| Living alone |
20 (4.4) |
9 (4.2) |
11 (4.7) |
73 (7.9) |
46 (9.2) |
27 (6.3) |
|
| Couple |
176 (38.9) |
88 (40.6) |
88 (37.3) |
280 (30.2) |
171 (34.3) |
109 (25.4) |
|
| Couple with child(ren) |
184 (40.6) |
76 (35.0) |
108 (45.8) |
348 (37.5) |
139 (27.9) |
209 (48.7) |
|
| Couple with child(ren) and grandchild(ren) |
20 (4.4) |
8 (3.7) |
12 (5.1) |
83 (8.9) |
59 (11.8) |
24 (5.6) |
|
| Other |
53 (11.7) |
36 (16.6) |
17 (7.2) |
144 (15.5) |
84 (16.8) |
60 (14.0) |
|
| Current smoker, n (%)
|
81 (17.9) |
39 (18.0) |
42 (17.8) |
26 (2.8) |
11 (2.2) |
15 (3.5) |
＜.001 |
| Current alcohol intake, n (%)
|
350 (77.3) |
157 (72.4) |
193 (81.8) |
383 (41.3) |
180 (36.1) |
203 (47.3) |
＜.001 |
| Physical activity level, METs h/day |
34.8 (4.7) |
35.1 (4.8) |
34.5 (4.6) |
36.0 (4.4) |
36.3 (4.9) |
35.7 (3.7) |
＜.001 |
| Menopause, n (%)
|
- |
- |
- |
635 (68.4) |
405 (81.2) |
230 (53.6) |
＜.001 |
| CAVI at baseline |
8.5 (1.2) |
8.8 (1.1) |
8.3 (1.2) |
8.0 (1.1) |
8.2 (1.0) |
7.7 (1.1) |
＜.001 |
| CAVI at follow-up§
|
8.7 (1.2) |
8.9 (1.2) |
8.5 (1.3) |
8.2 (1.2) |
8.4 (1.2) |
7.8 (1.0) |
＜.001 |
| Absolute difference of CAVI§
|
0.1 (0.8) |
0.1 (0.7) |
0.1 (0.9) |
0.2 (0.8) |
0.2 (0.9) |
0.1 (0.8) |
0.01 |
All values are mean (standard deviation).
CAVI, cardio-ankle vascular index. The higher value from either the right or left CAVI measurement was used.
§ Due to missing data at follow-up, n = 1377 was included in the analysis.
In the multivariable-adjusted mixed-effect models stratified by sex, the CAVI at baseline did not differ according to educational level in men or women. Similarly, educational level did not have a significant effect on the change in CAVI over time in either sex (Table 2). When further stratified by age at baseline (＜65 vs. ≥ 65 years), the main effects of educational level were not statistically significant in any of the 4 subgroups. However, the interaction term of educational level×time, which estimates the mean difference in the 5-year change in CAVI, was statistically significant (β=0.027, 95% confidence interval 0.001–0.053) among women of ≥ 65 years of age, but not in other subgroups (Table 3).
Table 2.Association between educational level and cardio-ankle vascular index by sex
| Men (n= 453)
|
| CAVI at baseline |
Difference |
(95% CI) |
P value
|
| Model 1 |
0.011 |
(-0.009 to 0.032) |
0.28 |
| Model 2 |
0.011 |
(-0.010 to 0.031) |
0.30 |
| Change in CAVI (per 5 years) |
Increase |
(95% CI) |
P value
|
| Model 1 |
-0.004 |
(-0.022 to 0.013) |
0.63 |
| Model 2 |
-0.005 |
(-0.022 to 0.013) |
0.60 |
| Women (n= 928)
|
| CAVI at baseline |
Difference |
(95% CI) |
P value
|
| Model 1 |
0.002 |
(-0.012 to 0.017) |
0.75 |
| Model 2 |
0.008 |
(-0.007 to 0.022) |
0.29 |
| Change in CAVI (per 5 years) |
Increase |
(95% CI) |
P value
|
| Model 1 |
0.004 |
(-0.011 to 0.018) |
0.63 |
| Model 2 |
0.004 |
(-0.011 to 0.018) |
0.63 |
CAVI, cardio-ankle vascular index; CI, confidence interval.
Model 1 was adjusted for age and the interaction term of age (baseline)×time.
Model 2 was adjusted as in Model 1＋body mass index, history of hypertension, history of diabetes mellitus, history of dyslipidemia, family
structure, current smoking status, current alcohol intake, physical activity level, and menopausal status (in women only). Differences or increases in CAVI (log-transformed value) are shown, with the higher educational group as the reference group.
Table 3.Association between educational level and cardio-ankle vascular index by sex and age
| Men
|
| CAVI at baseline |
Age at baseline ＜65 (n= 275)
|
Age at baseline ≥ 65 (n= 178)
|
| Difference |
(95% CI) |
P value
|
Difference |
(95% CI) |
P value
|
| Model 1 |
0.010 |
(-0.017 to 0.037) |
0.47 |
0.013 |
(-0.018 to 0.045) |
0.40 |
| Model 2 |
0.007 |
(-0.019 to 0.034) |
0.58 |
0.013 |
(-0.019 to 0.044) |
0.43 |
| Change in CAVI (per 5 years) |
Increase |
(95% CI) |
P value
|
Increase |
(95% CI) |
P value
|
| Model 1 |
-0.009 |
(-0.033 to 0.014) |
0.44 |
0.003 |
(-0.023 to 0.030) |
0.80 |
| Model 2 |
-0.009 |
(-0.033 to 0.014) |
0.43 |
0.003 |
(-0.023 to 0.029) |
0.82 |
| Women
|
| CAVI at baseline |
Age at baseline ＜65 (n= 656)
|
Age at baseline ≥ 65 (n= 272)
|
| Difference |
(95% CI) |
P value
|
Difference |
(95% CI) |
P value
|
| Model 1 |
0.007 |
(-0.010 to 0.024) |
0.44 |
-0.014 |
(-0.043 to 0.015) |
0.34 |
| Model 2 |
0.012 |
(-0.004 to 0.029) |
0.15 |
-0.010 |
(-0.039 to 0.018) |
0.47 |
| Change in CAVI (per 5 years) |
Increase |
(95% CI) |
P value
|
Increase |
(95% CI) |
P value
|
| Model 1 |
-0.003 |
(-0.021 to 0.014) |
0.73 |
0.027 |
(0.001 to 0.052) |
0.04 |
| Model 2 |
-0.003 |
(-0.021 to 0.014) |
0.72 |
0.027 |
(0.001 to 0.053) |
0.04 |
CAVI, cardio-ankle vascular index; CI, confidence interval.
Model 1 was adjusted for age and the interaction term of age (baseline)×time.
Model 2 was adjusted as in Model 1＋body mass index, history of hypertension, history of diabetes mellitus, history of dyslipidemia, family
structure, current smoking status, current alcohol intake, physical activity level, and menopausal status (in women only).
Differences or increases in CAVI (log-transformed value) are shown, with the higher educational group as the reference group.
Discussion
In this longitudinal analysis of a healthy sample of Japanese men and women, no difference in baseline CAVI was observed between those with higher and lower educational levels. However, women of ≥ 65 years of age with higher educational levels had significantly smaller increases in CAVI at 5 years than those with lower educational levels. The same association was not observed among women of ＜65 years of age or men.
Slower elevation of CAVI in older women with higher educational levels showed a protective effect against arterial stiffening over time. A previous study on IHD found that a greater part of the excess risk in poorly educated women was associated with blood pressure and cholesterol27). In fact, the percentage of women with hypertension and dyslipidemia included in our analyses was higher in the lower education group than in the higher education group. This may have been associated with the greater progression of arterial stiffness in the lower education group.
Another pathway may be the association between educational level and BMI. The proportion of obese women was higher among those with lower educational levels, consistent with a previous report16). As obesity accelerates the progression of atherosclerosis28), a lower BMI throughout their life course may have contributed to the slower development of arterial stiffness. Although we used the BMI at baseline for adjustment, prior BMI may have affected the outcome.
From a sociohistorical perspective, educational attainment in Japan 60 years ago was quite different from that today. Considering that the study participants who were 65 years of age at baseline were 18 years of age in 1962–1965, the average college or junior college enrollment rate for 1956–1965 was 18.4% for males and 7.2% for females, while for 1966–1975, the rate was 31.2% for males and 20.8% for females29). In line with this fact, very few women of ≥ 65 years of age at baseline received higher education (n=69/272, 25.4%) in comparison to younger women (n=360/656, 54.9%) and men (n=71/178, 39.9% and n=165/275, 60.0% in older and younger men, respectively) in our study. Additionally, the difference in the mean age between the higher and lower education groups in our study was higher for women (8.2 years) than for men (5.6 years). This suggests that educational differences may be larger in older female participants, possibly resulting in a significant impact on changes in CAVI among older women.
In terms of the biological mechanisms of arterial stiffness, the development of age-related arterial stiffness in women may reflect hormonal changes after the transition to menopause. It has been suggested that endogenous female hormone levels decrease and that the risk of postmenopausal atherosclerosis increases with age30, 31). One possible explanation for the difference in CAVI increase by educational level among women of ≥ 65 years of age in this study is that endogenous estrogen prevents arterial stiffness and protects blood vessels in younger premenopausal women, thus suppressing the development of arterial stiffness. However, this protective effect may be absent or weaker for those of ≥ 65 years of age, and the enhanced arterial stiffening manifested as an impact of lower educational attainment. Based on a previous study, the hazard ratio of cardiovascular events corresponding to our 10-year CAVI change of 0.6, which was estimated based on the multivariable-adjusted analysis of elderly women with lower educational levels, can be calculated as 1.2. As it has been reported that each 1% increase in HbA1c is associated with an 8% increase in the risk of heart failure32), a CAVI difference of 0.6 has clinical significance, constitutes a clinically relevant measure for a population with a low educational background.
Our results also suggested that the development of arterial stiffness may be more evident in postmenopausal women than in older men. Similar to estrogen levels in women, androgen levels in men decrease with age and are reportedly associated with arterial stiffening33). In addition, low testosterone levels are associated with lipid changes, such as low HDL cholesterol34), increased triglycerides35), hypertension36), insulin resistance37), and visceral fat accumulation38), which are risk factors for arterial stiffness. Based on these previous reports, it was expected that in men as in women, the development of arterial stiffness, which was suppressed by the protective effects of hormones at a young age, would become apparent with age. However, our results showed no difference between the lower and higher educational levels in the development of arterial stiffness in either the younger or older groups in men. The extent of the impact of hormones on arterial stiffening in association with educational disparities should be further investigated in the future using larger sample sizes.
As a psychological mechanism, educational level may be associated with literacy, lifestyle, and social support, which may mediate arterial stiffness. Previous studies have shown that older adults with lower health literacy have shorter educational histories and higher CAVI values39). A previous Japanese study also suggested that the association between higher CVD mortality and lower educational level can be explained by individuals’ weaker adherence to a healthy lifestyle40). Moreover, the association between education and health-related behaviors is reportedly mediated by social support41), which is associated with the extent of arterial stiffening42). Although we defined the higher education group as those who were educated in junior college, professional school, or 4-year college or higher, there are some variations within the group regarding career after graduation. For instance, it has been reported that starting salaries are higher for university graduates than for junior college graduates, or higher for those with a master’s or doctoral degree than for those with a bachelor’s degree among Japanese workers43). The same trend is also true for overall wages in relation to the educational background44). Generally, in Japan, a higher percentage of women graduate from junior college or professional school, while a higher percentage of men graduate from university or graduate school45). This trend was also observed in this study. Career differences may exist within the higher education group, depending on the type of school participants graduated from. Differences in work-related factors, such as work hours, responsibility, and income, may lead to differences in lifestyle, including smoking, alcohol, exercise, nutrition, and social factors, such as social support. As these factors can be confirmed to mediate the relationship between education and the progression of arterial stiffness, interventions targeting these factors may contribute to lowering the risk of arterial stiffness in women with low educational attainment that was observed in our study.
This study used a large sample of Japanese men and women with data on socioeconomic status and an index of arterial stiffness measured at two time points, allowing us to investigate longitudinal changes. However, a major limitation of this study is the possibility of residual confounding factors due to the presence of unmeasured physiological and environmental variables, such as genetic factors46) and regional differences47), which have been shown to be associated with arterial stiffness. Second, it is likely that those who participated in this study were healthier than those who did not because the participants were recruited on a voluntary basis. This is expected to create a null bias and limit the identification of significant but weaker associations with outcome measures. Third, our study included more women than men, which may have affected the results. Lastly, since we only had access to stiffness measures at baseline and 5 years later, intermediate changes, the shape of the arterial stiffness progression curve, and our ability to discern cause and effect are limited.
Conclusion
We found that educational level had a significant effect on the development of arterial stiffness over time among elderly women. However, in men and younger women, educational level did not show a substantial impact on arterial stiffness. The results suggest that the effects of education on arterial stiffness vary according to sex and age, with implications for the prevention of atherosclerotic diseases in elderly women with low socioeconomic status.
Acknowledgements
The authors thank all the participants and staff members of the Toon Health Study for their efforts in conducting the baseline survey and follow-up.
Notice of Grant Support
This study was supported by JSPS KAKENHI Grant Numbers JP16K09072, JP17KK0175, JP18H03056, JP18K10087, JP17K00881, and JP22H00496.
Conflict of Interest
All authors report no conflict of interest.
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